OpenRaiser/Intern-Atlas huggingface dataset:
Several Tunable GMM Kernels
Venue: arXiv cs.LG 2018
Year: 2018

While tree methods have been popular in practice, researchers and practitioners are also looking for simple algorithms which can reach similar accuracy of trees. In 2010, (Ping Li UAI'10) developed the method of "abc-robust-logitboost" and compared it with other supervised learning methods on datasets used by the deep learning literature. In this study, we propose a series of "tunable GMM kernels" which are simple and perform largely comparably to tree methods on the same datasets. Note that "abc-robust-logitboost" substantially improved the original "GDBT" in that (a) it developed a tree-split formula based on second-order information of the derivatives of the loss function; (b) it developed a new set of derivatives for multi-class classification formulation. In the prior study in 2017, the "generalized min-max" (GMM) kernel was shown to have good performance compared to the "radial-basis function" (RBF) kernel. However, as demonstrated in this paper, the original GMM kernel is often not as competitive as tree methods on the datasets used in the deep learning literature. Since the original GMM kernel has no parameters, we propose tunable GMM kernels by adding tuning parameters in various ways. Three basic (i.e., with only one parameter) GMM kernels are the "$e$GMM kernel", "$p$GMM kernel", and "$γ$GMM kernel", respectively. Extensive experiments show that they are able to produce good results for a large number of classification tasks. Furthermore, the basic kernels can be combined to boost the performance.
An Improved Statistic for the Pooled Triangle Test against PRNU-Copy Attack
Venue: arXiv cs.CR 2018
Year: 2018

We propose a new statistic to improve the pooled version of the triangle test used to combat the fingerprint-copy counter-forensic attack against PRNU-based camera identification [1]. As opposed to the original version of the test, the new statistic exploits the one-tail nature of the test, weighting differently positive and negative deviations from the expected value of the correlation between the image under analysis and the candidate images, i.e., those image suspected to have been used during the attack. The experimental results confirm the superior performance of the new test, especially when the conditions of the test are challenging ones, that is when the number of images used for the fingerprint-copy attack is large and the size of the image under test is small.
QUICK: QoS-guaranteed Efficient Cloudlet Placement in Wireless Metropolitan Area Networks
Venue: arXiv cs.NI 2018
Year: 2018

This article defines the QoS-guaranteed efficient cloudlet deploy problem in wireless metropolitan area network, which aims to minimize the average access delay of mobile users i.e. the average delay when service requests are successfully sent and being served by cloudlets. Meanwhile, we try to optimize total deploy cost represented by the total number of deployed cloudlets. For the first target, both un-designated capacity and constrained capacity cases are studied, and we have designed efficient heuristic and clustering algorithms respectively. We show our algorithms are more efficient than the existing algorithm. For the second target, we formulate an integer linear programming to minimize the number of used cloudlets with given average access delay requirement. A clustering algorithm is devised to guarantee the scalability. For a special case of the deploy cost optimization problem where all cloudlets' computing capabilities have been given, i.e., designated capacity, a minimal cloudlets efficient heuristic algorithm is further proposed. We finally evaluate the performance of proposed algorithms through extensive experimental simulations. Simulation results demonstrate the proposed algorithms are more than 46% efficient than existing algorithms on the average cloudlet access delay. Compared with existing algorithms, our proposed clustering and heuristic algorithms can reduce the number of deployed cloudlets by about 50% averagely.
Image Ordinal Classification and Understanding: Grid Dropout with Masking Label
Venue: arXiv cs.CV 2018
Year: 2018

Image ordinal classification refers to predicting a discrete target value which carries ordering correlation among image categories. The limited size of labeled ordinal data renders modern deep learning approaches easy to overfit. To tackle this issue, neuron dropout and data augmentation were proposed which, however, still suffer from over-parameterization and breaking spatial structure, respectively. To address the issues, we first propose a grid dropout method that randomly dropout/blackout some areas of the raining image. Then we combine the objective of predicting the blackout patches with classification to take advantage of the spatial information. Finally we demonstrate the effectiveness of both approaches by visualizing the Class Activation Map (CAM) and discover that grid dropout is more aware of the whole facial areas and more robust than neuron dropout for small training dataset. Experiments are conducted on a challenging age estimation dataset - Adience dataset with very competitive results compared with state-of-the-art methods.
Hierarchical Temporal Memory using Memristor Networks: A Survey
Venue: arXiv cs.AR 2018
Year: 2018

This paper presents a survey of the currently available hardware designs for implementation of the human cortex inspired algorithm, Hierarchical Temporal Memory (HTM). In this review, we focus on the state of the art advances of memristive HTM implementation and related HTM applications. With the advent of edge computing, HTM can be a potential algorithm to implement on-chip near sensor data processing. The comparison of analog memristive circuit implementations with the digital and mixed-signal solutions are provided. The advantages of memristive HTM over digital implementations against performance metrics such as processing speed, reduced on-chip area and power dissipation are discussed. The limitations and open problems concerning the memristive HTM, such as the design scalability, sneak currents, leakage, parasitic effects, lack of the analog learning circuits implementations and unreliability of the memristive devices integrated with CMOS circuits are also discussed.
An Efficient Secure Distributed Cloud Storage for Append-only Data
Venue: arXiv cs.CR 2018
Year: 2018

Cloud computing enables users (clients) to outsource large volume of their data to cloud servers. Secure distributed cloud storage schemes ensure that multiple servers store these data in a reliable and untampered fashion. We propose an idea to construct such a scheme for static data by encoding data blocks (using error-correcting codes) and then attaching authentication information (tags) to these encoded blocks. We identify some challenges while extending this idea to accommodate append-only data. Then, we propose our secure distributed cloud storage scheme for append-only data that addresses the challenges efficiently. The main advantage of our scheme is that it enables the servers to update the parity blocks themselves. Moreover, the client need not download any data (or parity) block to update the tags of the modified parity blocks residing on the servers. Finally, we analyze the security and performance of our scheme.
On Secure Exact-repair Regenerating Codes with a Single Pareto Optimal Point
Venue: arXiv cs.IT 2018
Year: 2018

The problem of exact-repair regenerating codes against eavesdropping attack is studied. The eavesdropping model we consider is that the eavesdropper has the capability to observe the data involved in the repair of a subset of $\ell$ nodes. An $(n,k,d,\ell)$ secure exact-repair regenerating code is an $(n,k,d)$ exact-repair regenerating code that is secure under this eavesdropping model. It has been shown that for some parameters $(n,k,d,\ell)$, the associated optimal storage-bandwidth tradeoff curve, which has one corner point, can be determined. The focus of this paper is on characterizing such parameters. We establish a lower bound $\hat{\ell}$ on the number of wiretap nodes, and show that this bound is tight for the case $k = d = n-1$.
Online Connected Dominating Set Leasing
Venue: arXiv cs.DS 2018
Year: 2018

We introduce the \emph{Online Connected Dominating Set Leasing} problem (OCDSL) in which we are given an undirected connected graph $G = (V, E)$, a set $\mathcal{L}$ of lease types each characterized by a duration and cost, and a sequence of subsets of $V$ arriving over time. A node can be leased using lease type $l$ for cost $c_l$ and remains active for time $d_l$. The adversary gives in each step $t$ a subset of nodes that need to be dominated by a connected subgraph consisting of nodes active at time $t$. The goal is to minimize the total leasing costs. OCDSL contains the \emph{Parking Permit Problem}~\cite{PPP} as a special subcase and generalizes the classical offline \emph{Connected Dominating Set} problem~\cite{Guha1998}. It has an $Ω(\log ^2 n + \log |\mathcal{L}|)$ randomized lower bound resulting from lower bounds for the \emph{Parking Permit Problem} and the \emph{Online Set Cover} problem~\cite{Alon:2003:OSC:780542.780558,Korman}, where $|\mathcal{L}|$ is the number of available lease types and $n$ is the number of nodes in the input graph. We give a randomized $\mathcal{O}(\log ^2 n + \log |\mathcal{L}| \log n)$-competitive algorithm for OCDSL. We also give a deterministic algorithm for a variant of OCDSL in which the dominating subgraph need not be connected, the \emph{Online Dominating Set Leasing} problem. The latter is based on a simple primal-dual approach and has an $\mathcal{O}(|\mathcal{L}| \cdot Δ)$-competitive ratio, where $Δ$ is the maximum degree of the input graph.
Category-Based Deep CCA for Fine-Grained Venue Discovery from Multimodal Data
Venue: arXiv cs.CV 2018
Year: 2018

In this work, travel destination and business location are taken as venues. Discovering a venue by a photo is very important for context-aware applications. Unfortunately, few efforts paid attention to complicated real images such as venue photos generated by users. Our goal is fine-grained venue discovery from heterogeneous social multimodal data. To this end, we propose a novel deep learning model, Category-based Deep Canonical Correlation Analysis (C-DCCA). Given a photo as input, this model performs (i) exact venue search (find the venue where the photo was taken), and (ii) group venue search (find relevant venues with the same category as that of the photo), by the cross-modal correlation between the input photo and textual description of venues. In this model, data in different modalities are projected to a same space via deep networks. Pairwise correlation (between different modal data from the same venue) for exact venue search and category-based correlation (between different modal data from different venues with the same category) for group venue search are jointly optimized. Because a photo cannot fully reflect rich text description of a venue, the number of photos per venue in the training phase is increased to capture more aspects of a venue. We build a new venue-aware multimodal dataset by integrating Wikipedia featured articles and Foursquare venue photos. Experimental results on this dataset confirm the feasibility of the proposed method. Moreover, the evaluation over another publicly available dataset confirms that the proposed method outperforms state-of-the-arts for cross-modal retrieval between image and text.
Decidability of the existential fragment of some infinitely generated trace monoids: an application to ordinals
Venue: arXiv cs.LO 2018
Year: 2018

Diekert, Matiyasevich and Muscholl proved that the existential first-order theory of a trace monoid over a finite alphabet is decidable. We extend this result to a natural class of trace monoids with infinitely many generators. As an application, we prove that for every ordinal $λ$ less than $\varepsilon_0$, the existential theory of the set of successor ordinals less than $λ$ equipped with multiplication is decidable.
Efficient Design of Hardware-Enabled Reservoir Computing in FPGAs
Venue: arXiv cs.ET 2018
Year: 2018

In this work, we propose a new approach towards the efficient optimization and implementation of reservoir computing hardware reducing the required domain expert knowledge and optimization effort. First, we adapt the reservoir input mask to the structure of the data via linear autoencoders. We therefore incorporate the advantages of dimensionality reduction and dimensionality expansion achieved by conventional algorithmically efficient linear algebra procedures of principal component analysis. Second, we employ evolutionary-inspired genetic algorithm techniques resulting in a highly efficient optimization of reservoir dynamics with dramatically reduced number of evaluations comparing to exhaustive search. We illustrate the method on the so-called single-node reservoir computing architecture, especially suitable for implementation in ultrahigh-speed hardware. The combination of both methods and the resulting reduction of time required for performance optimization of a hardware system establish a strategy towards machine learning hardware capable of self-adaption to optimally solve specific problems. We confirm the validity of those principles building reservoir computing hardware based on a field-programmable gate array.
Binary Sparse Bayesian Learning Algorithm for One-bit Compressed Sensing
Venue: arXiv cs.IT 2018
Year: 2018

In this letter, a binary sparse Bayesian learning (BSBL) algorithm is proposed to slove the one-bit compressed sensing (CS) problem in both single measurement vector (SMV) and multiple measurement vectors (MMVs). By utilising the Bussgang-like decomposition, the one-bit CS problem can be approximated as a standard linear model. Consequently, the standard SBL algorithm can be naturally incorporated. Numerical results demonstrate the effectiveness of the BSBL algorithm.
Tiered-Latency DRAM: Enabling Low-Latency Main Memory at Low Cost
Venue: arXiv cs.AR 2018
Year: 2018

This paper summarizes the idea of Tiered-Latency DRAM (TL-DRAM), which was published in HPCA 2013, and examines the work's significance and future potential. The capacity and cost-per-bit of DRAM have historically scaled to satisfy the needs of increasingly large and complex computer systems. However, DRAM latency has remained almost constant, making memory latency the performance bottleneck in today's systems. We observe that the high access latency is not intrinsic to DRAM, but a trade-off is made to decrease the cost per bit. To mitigate the high area overhead of DRAM sensing structures, commodity DRAMs connect many DRAM cells to each sense amplifier through a wire called a bitline. These bit-lines have a high parasitic capacitance due to their long length, and this bitline capacitance is the dominant source of DRAM latency. Specialized low-latency DRAMs use shorter bitlines with fewer cells, but have a higher cost-per-bit due to greater sense amplifier area overhead. To achieve both low latency and low cost per bit, we introduce Tiered-Latency DRAM (TL-DRAM). In TL-DRAM, each long bitline is split into two shorter segments by an isolation transistor, allowing one of the two segments to be accessed with the latency of a short-bitline DRAM without incurring a high cost per bit. We propose mechanisms that use the low-latency segment as a hardware-managed or software-managed cache. Our evaluations show that our proposed mechanisms improve both performance and energy efficiency for both single-core and multiprogrammed workloads. Tiered-Latency DRAM has inspired several other works on reducing DRAM latency with little to no architectural modification.
Overcoming the Imbalance Between Tag Recommendation Approaches and Real-World Folksonomy Structures with Cognitive-Inspired Algorithms
Venue: arXiv cs.IR 2018
Year: 2018

In this paper, we study the imbalance between current state-of-the-art tag recommendation algorithms and the folksonomy structures of real-world social tagging systems. While algorithms such as FolkRank are designed for dense folksonomy structures, most social tagging systems exhibit a sparse nature. To overcome this imbalance, we show that cognitive-inspired algorithms, which model the tag vocabulary of a user in a cognitive-plausible way, can be helpful. Our present approach does this via implementing the activation equation of the cognitive architecture ACT-R, which determines the usefulness of units in human memory (e.g., tags). In this sense, our long-term research goal is to design hybrid recommendation approaches, which combine the advantages of both worlds in order to adapt to the current setting (i.e., sparse vs. dense ones).
Optimization of Occlusion-Inducing Depth Pixels in 3-D Video Coding
Venue: arXiv cs.MM 2018
Year: 2018

The optimization of occlusion-inducing depth pixels in depth map coding has received little attention in the literature, since their associated texture pixels are occluded in the synthesized view and their effect on the synthesized view is considered negligible. However, the occlusion-inducing depth pixels still need to consume the bits to be transmitted, and will induce geometry distortion that inherently exists in the synthesized view. In this paper, we propose an efficient depth map coding scheme specifically for the occlusion-inducing depth pixels by using allowable depth distortions. Firstly, we formulate a problem of minimizing the overall geometry distortion in the occlusion subject to the bit rate constraint, for which the depth distortion is properly adjusted within the set of allowable depth distortions that introduce the same disparity error as the initial depth distortion. Then, we propose a dynamic programming solution to find the optimal depth distortion vector for the occlusion. The proposed algorithm can improve the coding efficiency without alteration of the occlusion order. Simulation results confirm the performance improvement compared to other existing algorithms.
Distributed Multiple Access with Multiple Transmission Options at The Link Layer
Venue: arXiv cs.IT 2018
Year: 2018

This paper investigates the problem of distributed medium access control in a wireless multiple access network with an unknown finite number of homogeneous transmitters. An enhanced physical link layer interface is considered where each link layer user can be equipped with multiple transmission options. Assume that each user is backlogged with a saturated message queue. With a generally-modeled channel, a distributed medium access control framework is suggested to adapt the transmission scheme of each user to maximize an arbitrarily chosen symmetric network utility. The proposed framework suggests that the receiver should measure the success probability of a carefully designed virtual packet, and feed such information back to the transmitters. Upon receiving the measured probability, each transmitter should obtain a user number estimate by comparing the probability with its theoretical value, and then adapt its transmission scheme accordingly. Conditions under which the proposed algorithm should converge to a designed unique equilibrium are characterized. Simulation results are provided to demonstrate the optimality and the convergence properties of the proposed algorithm.
High-resolution medical image synthesis using progressively grown generative adversarial networks
Venue: arXiv cs.CV 2018
Year: 2018

Generative adversarial networks (GANs) are a class of unsupervised machine learning algorithms that can produce realistic images from randomly-sampled vectors in a multi-dimensional space. Until recently, it was not possible to generate realistic high-resolution images using GANs, which has limited their applicability to medical images that contain biomarkers only detectable at native resolution. Progressive growing of GANs is an approach wherein an image generator is trained to initially synthesize low resolution synthetic images (8x8 pixels), which are then fed to a discriminator that distinguishes these synthetic images from real downsampled images. Additional convolutional layers are then iteratively introduced to produce images at twice the previous resolution until the desired resolution is reached. In this work, we demonstrate that this approach can produce realistic medical images in two different domains; fundus photographs exhibiting vascular pathology associated with retinopathy of prematurity (ROP), and multi-modal magnetic resonance images of glioma. We also show that fine-grained details associated with pathology, such as retinal vessels or tumor heterogeneity, can be preserved and enhanced by including segmentation maps as additional channels. We envisage several applications of the approach, including image augmentation and unsupervised classification of pathology.
An Empirical Analysis of Anonymity in Zcash
Venue: arXiv cs.CR 2018
Year: 2018

Among the now numerous alternative cryptocurrencies derived from Bitcoin, Zcash is often touted as the one with the strongest anonymity guarantees, due to its basis in well-regarded cryptographic research. In this paper, we examine the extent to which anonymity is achieved in the deployed version of Zcash. We investigate all facets of anonymity in Zcash's transactions, ranging from its transparent transactions to the interactions with and within its main privacy feature, a shielded pool that acts as the anonymity set for users wishing to spend coins privately. We conclude that while it is possible to use Zcash in a private way, it is also possible to shrink its anonymity set considerably by developing simple heuristics based on identifiable patterns of usage.
LISA: Increasing Internal Connectivity in DRAM for Fast Data Movement and Low Latency
Venue: arXiv cs.AR 2018
Year: 2018

This paper summarizes the idea of Low-Cost Interlinked Subarrays (LISA), which was published in HPCA 2016, and examines the work's significance and future potential. Contemporary systems perform bulk data movement movement inefficiently, by transferring data from DRAM to the processor, and then back to DRAM, across a narrow off-chip channel. The use of this narrow channel results in high latency and energy consumption. Prior work proposes to avoid these high costs by exploiting the existing wide internal DRAM bandwidth for bulk data movement, but the limited connectivity of wires within DRAM allows fast data movement within only a single DRAM subarray. Each subarray is only a few megabytes in size, greatly restricting the range over which fast bulk data movement can happen within DRAM. Our HPCA 2016 paper proposes a new DRAM substrate, Low-Cost Inter-Linked Subarrays (LISA), whose goal is to enable fast and efficient data movement across a large range of memory at low cost. LISA adds low-cost connections between adjacent subarrays. By using these connections to interconnect the existing internal wires (bitlines) of adjacent subarrays, LISA enables wide-bandwidth data transfer across multiple subarrays with little (only 0.8%) DRAM area overhead. As a DRAM substrate, LISA is versatile, enabling a variety of new applications. We describe and evaluate three such applications in detail: (1) fast inter-subarray bulk data copy, (2) in-DRAM caching using a DRAM architecture whose rows have heterogeneous access latencies, and (3) accelerated bitline precharging by linking multiple precharge units together. Our extensive evaluations show that each of LISA's three applications significantly improves performance and memory energy efficiency on a variety of workloads and system configurations.